Magnetic resonance imaging allows anatomical and physiological features of living human and animal bodies to be observed. Recently, there has been interest in the magnetic resonance technique known as magnetic resonance diffusion imaging for the detection of cancer where the image signal is dependent upon the diffusivity of the tissue. Diffusion imaging includes diffusion weighted imaging (DWI), diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), q-space imaging, and many other diffusion techniques. For the purpose of clarity and brevity, the description hereinafter is described with particular reference to DWI. It is to be understood that this description is for illustrative purposes and also finds application in other usage scenarios and/or diffusion imaging techniques.
DWI measures the magnitude of random motion (Brownian motion) of water molecules, which is often referred to as apparent diffusion coefficient (ADC). The physiological basis of using DWI for cancer diagnosis is that the densely packed cells within a cancer restrict the normal random motion. A low level of random motion is an indicator of cancer. DWI acquisitions are defined by their b-values, where the b-value is defined by the amplitude, duration and temporal spacing of the DWI gradients which allows for the probing of different diffusion coefficients. DWI images with different b-values and different diffusion directions are normally combined with one another in order to calculate ADC maps. Moreover, researchers have proposed many models to describe the complicated diffusion in human tissues, such as the mono-exponential model which refers to the so-called ADC map, and the bi-exponential model which refers to IVIM map, DTI map, and DKI map, which are collectively referred to as diffusion parameter map herein. The IVIM model is preferred in tissues with high perfusion, DKI model has been investigated in liver, and DTI model has been studied in brain mostly. Diffusion weighted images and/or diffusion parameter maps can be used by radiologists to distinguish areas with low random motion that are suspicious for cancer.
DWI collected at higher b-values, e.g., greater than 1000 s/mm2, allows for increased delineation between tumors and normal tissues. However, a challenge faced in using high b-values is that the acquired images at high b-values have low signal-to-noise ratio (SNR) and serious distortion. An alternative approach to achieve high b-value DWI is computed DWI in which diffusion weighted images using high b-values can be mathematically derived from lower b-value DWI images using a computational model, rather than directly acquired. Although many studies have suggested the images generated by computed DWI are diagnostically comparable to acquired high b-value DWI, radiologists still have no enough confidence in feasibility of computed DWI since the computed DWI is only a simulation of acquired high b-value DWI. Moreover, the computed DWI often results in abnormal contrast, which further undermines radiologists' confidence in computed DWI. Furthermore, the image quality of computed DWI depends on computational models, b-values, tissues of interest, etc., which calls for more clinical investigation to evaluate the efficacy of computed DWI before it is widely accepted.
“Improved Multi B-Value Diffusion-Weighted MRI of the Body by Simultaneous Model Estimation and Image Reconstruction” by Freiman Moti discloses a Bayesian model of the expected signal with the signal decay model utilized as the prior information, which allows a simultaneous model estimation and image reconstruction for multiple b-values at once.